def actor_boundary(env, actor, epsoides=1000, steps=100):
max_boundary = np.zeros([env.state_dim, 1])
min_boundary = np.zeros([env.state_dim, 1])
for ep in xrange(epsoides):
s = env.reset()
max_boundary, min_boundary = metrics.find_boundary(
s, max_boundary, min_boundary)
for i in xrange(steps):
a = actor.predict(np.reshape(np.array(s), (1, actor.s_dim))) #+ actor_noise()
s, _, terminal = env.step(a.reshape(actor.a_dim, 1))
max_boundary, min_boundary = metrics.find_boundary(
s, max_boundary, min_boundary)
if terminal:
break
print('max_boundary:\n{}\nmin_boundary:\n{}'.format(
max_boundary, min_boundary))
def shield_boundary(self, sample_ep=500, sample_step=100):
"""sample to find the state bound of shield
Args:
sample_ep (int, optional): epsoides
sample_step (int, optional): step in each epsoide
"""
max_boundary = np.zeros([self.env.state_dim, 1])
min_boundary = np.zeros([self.env.state_dim, 1])
for ep in xrange(sample_ep):
x = self.env.reset()
for i in xrange(sample_step):
u = self.call_shield(x)
max_boundary, min_boundary = metrics.find_boundary(
x, max_boundary, min_boundary)
# step
x, _, terminal = self.env.step(u)
print('max_boundary:\n{}\nmin_boundary:\n{}'.format(
max_boundary, min_boundary))